# Multi-Target Prediction: A Unifying View on Problems and Methods

arXiv: https://arxiv.org/abs/1809.02352 · HTML: https://mlsanity.com/paper/1809.02352/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 15 of 15)

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- [Multivariate Uncertainty Quantification with Tomographic Quantile Forests](https://mlsanity.com/paper/2512.16383/) (2025)
- [Robust Regression via Model Based Methods](https://mlsanity.com/paper/2106.10759/) (2021)
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- [Estimating Multi-label Accuracy using Labelset Distributions](https://mlsanity.com/paper/2209.04163/) (2022)
- [Hyperparameter optimization in deep multi-target prediction](https://mlsanity.com/paper/2211.04362/) (2022)
- [Local Interpretability of Random Forests for Multi-Target Regression](https://mlsanity.com/paper/2303.16506/) (2023)
- [Direct side information learning for zero-shot regression](https://mlsanity.com/paper/2402.01264/) (2024)

## Abstract (arXiv)

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.